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Generic Protocol for Optimization of Heterologous Protein Production Using Automated Microbioreactor Technology

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Multi-objective optimization of an industrial penicillin V bioreactor train using non-dominated sorting genetic

Fook Choon Lee¹, Gade Pandu Rangaiah, Ajay Kumar Ray

¹Department of Chemical and Biomolecular Engineering, National University of Singapore, Engineering Drive 4, Singapore 117576, Republic of Singapore.

Biotechnology and Bioengineering

|April 4, 2007

Summary

This study optimizes industrial penicillin V bioreactor trains for multiple objectives, yielding a range of optimal conditions. This approach improves efficiency and profit in penicillin production.

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Area of Science:

Biotechnology and biochemical engineering
Industrial microbiology
Process optimization

Background:

Penicillin V is a key raw material for semi-synthetic antibiotics like amoxicillin and cephalosporins.
Industrial penicillin production relies on complex bioreactor systems.
Optimizing these systems is crucial for economic viability and antibiotic supply.

Purpose of the Study:

To optimize an industrial penicillin V bioreactor train for multiple objectives simultaneously.
To explore trade-offs between key performance indicators (KPIs) in penicillin production.
To identify optimal design and operating conditions for enhanced efficiency and profitability.

Main Methods:

Modeling the penicillin fermentation process using a morphologically structured mechanism.
Employing the elitist non-dominated sorting genetic algorithm II (NSGA-II) for multi-objective optimization (two and three objectives).
Analyzing an industrial semi-continuous, synchronously operated bioreactor train.

Main Results:

A wide range of optimal design and operating conditions were successfully obtained, moving beyond single-optimum traditional methods.
Demonstrated trade-offs between critical KPIs including batch cycle time, yield, profit, and penicillin concentration.
Detailed discussion on the impact of design and operating variables on the identified optimal solutions.

Conclusions:

Multi-objective optimization using NSGA-II provides a comprehensive set of optimal solutions for industrial penicillin V production.
The methodology allows for informed decision-making by visualizing trade-offs between competing objectives.
This approach can lead to significant improvements in the efficiency and economic performance of antibiotic manufacturing.